Molecular docking tools are regularly used to computationally identify new molecules in virtual screening for drug discovery. However, docking tools suffer from inaccurate scoring functions with widely varying performance on different proteins. To enable more accurate ranking of active over inactive ligands in virtual screening, we created a machine learning consensus docking tool, MILCDock, that uses predictions from five traditional molecular docking tools to predict the probability a ligand binds to a protein. MILCDock was trained and tested on data from both the DUD-E and LIT-PCBA docking datasets and shows improved performance over traditional molecular docking tools and other consensus docking methods on the DUD-E dataset. LIT-PCBA targets proved to be difficult for all methods tested. We also find that DUD-E data, although biased, can be effective in training machine learning tools if care is taken to avoid DUD-E’s biases during training.

中文翻译：

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MILCDock：机器学习增强了药物发现虚拟筛选的共识对接

分子对接工具通常用于在药物发现的虚拟筛选中计算识别新分子。然而，对接工具的评分功能不准确，对不同蛋白质的性能差异很大。为了在虚拟筛选中更准确地对活性配体和非活性配体进行排名，我们创建了一个机器学习共识对接工具 MILCDock，它使用来自五种传统分子对接工具的预测来预测配体与蛋白质结合的概率。MILCDock 在 DUD-E 和 LIT-PCBA 对接数据集的数据上进行了训练和测试，并在 DUD-E 数据集上显示出优于传统分子对接工具和其他共识对接方法的性能。LIT-PCBA 目标被证明对于所有测试的方法都是困难的。我们还发现 DUD-E 数据虽然有偏见，